Home > Computer Science > Data Miining > Volume-3 > Issue-3 > Association Rule Hiding using Hash Tree

Association Rule Hiding using Hash Tree

Call for Papers

Volume-8 | Issue-3

Last date : 26-Jun-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Association Rule Hiding using Hash Tree


Garvit Khurana

https://doi.org/10.31142/ijtsrd23037



Garvit Khurana "Association Rule Hiding using Hash Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3, April 2019, pp.787-789, URL: https://www.ijtsrd.com/papers/ijtsrd23037.pdf

As extensive chronicles of information contain classified rules that must be protected before distributed, association rule hiding winds up one of basic privacy preserving data mining issues. Information sharing between two associations is ordinary in various application zones for instance business planning or marketing. Profitable overall patterns can be found from the incorporated dataset. In any case, some delicate patterns that ought to have been kept private could likewise be uncovered. Vast disclosure of touchy patterns could diminish the forceful limit of the information owner. Database outsourcing is becoming a necessary business approach in the ongoing distributed and parallel frameworks for incessant things identification. This paper focuses on introducing a few adjustments to safeguard both customer and server privacy. Adjustment strategies like hash tree to existing APRIORI algorithm are recommended that will be helping in safeguarding the accuracy, utility loss and data privacy and result is generated in small execution time. We implement the modified algorithm to two custom datasets of different sizes.

Association Rule Mining, Modified APRIORI, Frequent Itemset Mining, Hash Tree


IJTSRD23037
Volume-3 | Issue-3, April 2019
787-789
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin